This is a collection of Python scripts for performing the proposed method DNN-MYK for controlled response selection.
- demo_code.py contains code for implementing the method on simulated data.
| Parameters | Description |
|---|---|
| n | Sample size |
| r | Total number of responses |
| p | Total number of features |
| m | Total number of important responses |
| rho | Correlation in X |
| betaValue | The absolute values of the coefficients |
| t | Sparsity level in the coefficients beta vector |
We vary the key parameter values with different levels below in both linear and nonlinear settings.
| Parameters | Levels |
|---|---|
| Number of responses (r) | 1000, 1500, 2000, 2500, 3000 |
| Sample size (n) | 300, 400, 500, 600 |
| Correlation (rho) in X | 0.1, 0.3, 0.5, 0.7, 0.9 |
| Sparsity level (t) in coefficients beta | 0.1, 0.3, 0.5, 0.7, 0.9 |
By varying the key parameters and run demo.py in the linear setting, we show the results in the following figure under a linear setting. In our paper, we also explore nonlinear settings.
